Recently, tree model is revisited to speed up the deployment of deep models in the recommendation system. Nevertheless, the query's feature used in our CAN is a set of discrete local features, and thus cannot be represented as an embedding feature to build a tree as TDM [1]}, [2]}. To tackle this problem, we propose a novel dual-path CAN to make it compatible with tree-based model. Based on it, we build Tree-based Combo-Attention Network (TCAN), which is recently launched in Baidu's dynamic video advertising platform. It achieves high effectiveness by using the combo-attention network (CAN), and meanwhile achieves high efficiency by exploiting the tree structure which avoids exhaustively comparing with each sample in the database.
We visualize the overview of the proposed TCAN in Figure REF . Basically, given a query, it traverses the tree from the root to leafs, the combo-attention network is only computed on nodes along the traverse trace, which avoid exhaustively comparing the query with all videos in the database. Given \(N\)  reference videos in the database, the tree model decreases the computation complexity from \(\mathcal {O}(N)\)  to \(\mathcal {O}(\mathrm {log}(N))\) , making it feasible for online serving. After launching the TCAN, we achieve a \(2.26\%\)  increase in CTR, and a \(2.63\%\)  increase in CVR.
